Correlations between quantitative fat–water magnetic resonance imaging and computed tomography in human subcutaneous white adipose tissue

Beyond estimation of depot volumes, quantitative analysis of adipose tissue properties could improve understanding of how adipose tissue correlates with metabolic risk factors. We investigated whether the fat signal fraction (FSF) derived from quantitative fat–water magnetic resonance imaging (MRI) scans at 3.0 T correlates to CT Hounsfield units (HU) of the same tissue. These measures were acquired in the subcutaneous white adipose tissue (WAT) at the umbilical level of 21 healthy adult subjects. A moderate correlation exists between MRI- and CT-derived WAT values for all subjects, R2=0.54, p\u3c0.0001, with a slope of −2.6, (95% CI [−3.3,−1.8]), indicating that a decrease of 1 HU equals a mean increase of 0.38% FSF. We demonstrate that FSF estimates obtained using quantitative fat–water MRI techniques correlate with CT HU values in subcutaneous WAT, and therefore, MRI-based FSF could be used as an alternative to CT HU for assessing metabolic risk factors

Characterizing active and inactive brown adipose tissue in adult humans using PET-CT and MR imaging

Activated brown adipose tissue (BAT) plays an important role in thermogenesis and whole body metabolism in mammals. Positron emission tomography (PET)-computed tomography (CT) imaging has identified depots of BAT in adult humans, igniting scientific interest. The purpose of this study is to characterize both active and inactive supraclavicular BAT in adults and compare the values to those of subcutaneous white adipose tissue (WAT). We obtained [18F]fluorodeoxyglucose ([18F]FDG) PET-CT and magnetic resonance imaging (MRI) scans of 25 healthy adults. Unlike [18F]FDG PET, which can detect only active BAT, MRI is capable of detecting both active and inactive BAT. The MRI-derived fat signal fraction (FSF) of active BAT was significantly lower than that of inactive BAT (means ± SD; 60.2 ± 7.6 vs. 62.4 ± 6.8%, respectively). This change in tissue morphology was also reflected as a significant increase in Hounsfield units (HU; −69.4 ± 11.5 vs. −74.5 ± 9.7 HU, respectively). Additionally, the CT HU, MRI FSF, and MRI R2* values are significantly different between BAT and WAT, regardless of the activation status of BAT. To the best of our knowledge, this is the first study to quantify PET-CT and MRI FSF measurements and utilize a semiautomated algorithm to identify inactive and active BAT in the same adult subjects. Our findings support the use of these metrics to characterize and distinguish between BAT and WAT and lay the foundation for future MRI analysis with the hope that some day MRI-based delineation of BAT can stand on its own

Genetically determined serum urate levels and cardiovascular and other diseases in UK Biobank cohort:A phenome-wide mendelian randomization study

BACKGROUND: The role of urate in cardiovascular diseases (CVDs) has been extensively investigated in observational studies; however, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance. METHODS AND FINDINGS: A phenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic model (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 unrelated White British individuals (54% female) in the UK Biobank who were aged 40-69 years (mean age, 56.87; SD, 7.99) when recruited from 2006 to 2010. Mendelian randomization (MR) analyses were performed to replicate significant findings using various genome-wide association study (GWAS) consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for urate. PheWAS analysis, examining the association with 1,431 disease outcomes, identified 13 distinct phecodes representing 4 disease groups (inflammatory polyarthropathies, hypertensive disease, circulatory disease, and metabolic disorders) and 9 disease outcomes (gout, gouty arthropathy, pyogenic arthritis, essential hypertension, coronary atherosclerosis, ischemic heart disease, chronic ischemic heart disease, myocardial infarction, and hypercholesterolemia) that were associated with genetically determined serum urate levels after multiple testing correction (p < 3.35 × 10-4). TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases, and blood lipid levels but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with CVDs. The main limitations of this study relate to possible bias from pleiotropic effects of the considered genetic variants and possible misclassification of cases for mild disease that did not require hospitalization. CONCLUSION: In this study, high serum urate levels were found to be associated with increased risk of different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a common mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities

Phenome-wide association study (PheWAS) on the genetic determinants of serum urate level and disease outcomes in UK Biobank

IntroductionElevated serum uric acid (SUA) concentration, known as hyperuricaemia, is a common abnormity in individuals with metabolic disorders. There is increasing evidence supporting the link between high SUA level and the increased risk of a wide range of clinical disorders, including hypertension, cardiovascular diseases (CVD), chronic renal diseases and metabolic syndrome. Although there are considerable research efforts in understanding the pathogenic pathways of high SUA level and the related clinical consequences, their causal relationships have not been established except for gout. Like other complex traits, genetic determinants play a substantial role (an estimated heritability of 40-70%) in the regulation of SUA level. Investigating the role of genetic variants related to SUA in various diseases might provide evidence for the above hypothesis which links uric acid to clinical disorders. Method Umbrella review was carried out first to provide a comprehensive overview on the range of health outcomes in relation to SUA level by incorporating evidence from systematic reviews and meta-analyses of observational studies, meta-analyses of randomised controlled trials (RCTs), and Mendelian randomisation (MR) studies. The umbrella review summarised the range of related health outcomes, the magnitude, direction and significance of identified associations and effects, and classified the evidence into four categories (class I [convincing], II [highly suggestive], III [suggestive], and IV [weak]) with assessment of multiple sources of biases. Then, a MR-PheWAS (Phenome-wide association study incorporated with Mendelian randomisation [MR]) was performed to investigate the associations between the 31 SUA genetic risk variants and a very wide range of disease outcomes by using the interim release data of UK Biobank (n=120,091). The SUA genetic risk loci were employed as instruments individually. The framework of phenome was defined by the PheCODE schema using the International Classification of Diseases (ICD) diagnosis codes documented in the health records of UK Biobank. Phenome-wide association test was performed first to identify any association across the SUA genetic risk loci and the phenome; MR design and HEIDI (heterogeneity in dependent instruments) tests were then applied to distinguish the PheWAS associations that were due to causality, pleiotropy or genetic linkage.To validate the MR-PheWAS findings, an enlarged Phenome-wide Mendelian randomisation (PWMR) analysis were performed by using data from the full UK Biobank cohort (n=339,256). A weighted polygenic risk score (GRS), incorporating effect estimates of multiple genetic risk loci, was employed as a proxy of the SUA level. The framework of phenome was defined by both the PheCODE schema and an alternative Tree-structured phenotypic model (TreeWAS) for analysis. Significant associations from these analyses were taken forward for replication in different populations by analysing data from various GWAS consortia documented in the MR-base database. Sensitivity analyses examining the pleiotropic effects of urate genetic risk loci on a set of metabolic traits were performed to explore any causal effects and pleiotropic associations.ResultsThe umbrella review included 101 articles and comprised 144 meta-analyses of observational studies, 31 meta-analyses of randomised controlled trials and 107 Mendelian randomisation studies. This remarkable assembly of evidence explored 136 unique health outcomes and reported convincing (class I) evidence for the causal role of SUA in gout and nephrolithiasis. Furthermore, highly suggestive (class II) evidence was reported for five health outcomes, in which high SUA level was associated with increased risk of heart failure, hypertension, impaired fasting glucose or diabetes, chronic kidney disease, and coronary heart disease mortality in the general population. The remaining 129 associations were classified as either suggestive or weak. The MR-PheWAS (using the interim release cohort) identified 25 disease groups/ outcomes to be associated with SUA genetic risk loci after multiple testing correction (p<8.6 ×10-5). The MR IVW (inverse variance weighted) analysis implicated a causal role of SUA level in three disease groups: inflammatory polyarthropathies (OR=1.22, 95% CI: 1.11 to 1.34), hypertensive disease (OR=1.08, 95% CI: 1.03 to 1.14) and disorders of metabolism (OR=1.07, 95% CI: 1.01 to 1.14); and four disease outcomes: gout (OR=4.88, 95% CI: 3.91 to 6.09), essential hypertension (OR=1.08, 95% CI: 1.03 to 1.14), myocardial infarction (OR=1.16, 95% CI: 1.03 to 1.30) and coeliac disease (OR=1.41, 95% CI: 1.05 to 1.89). After balancing pleiotropic effects in MR Egger analysis, only gout and its encompassing disease group of inflammatory polyarthropathies were considered to be causally associated with SUA level. The analysis also highlighted a locus (ATXN2/S2HB3) that may influence SUA level and multiple cardiovascular and autoimmune diseases via pleiotropy.The PWMR analysis, using data from the full UK Biobank cohort (n=339,256), examining the association with 1,431 disease outcomes, identified 13 phecodes that were associated with the weighted GRS of SUA level with the p value passing the significance threshold of PheWAS (p<3.4×10-4). These phecodes represent 4 disease groups: inflammatory polyarthropathies (OR=1.28; 95% CI: 1.21 to 1.35; p=4.97×10-19), hypertensive disease (OR=1.08; 95% CI: 1.05 to 1.11; p=6.02×10-7), circulatory disease (OR=1.05; 95% CI: 1.02 to 1.07; p=3.29×10-4) and metabolic disorders (OR=1.07; 95% CI: 1.03 to 1.11; p= 3.33×10-4), and 9 disease outcomes: gout (OR=5.37; 95% CI: 4.67 to 6.18; p= 4.27×10-123), gouty arthropathy (OR=5.11; 95% CI: 2.45 to 10.66; p=1.39×10-5), pyogenic arthritis (OR=2.10; 95% CI: 1.41 to 3.14; p=2.87×10-4), essential hypertension (OR=1.08; 95% CI: 1.05 to 1.11; p=6.62×10-7), coronary atherosclerosis (OR=1.10; 95% CI: 1.05 to 1.15; p=1.17×10-5), ischaemic heart disease (OR=1.10, 95% CI: 1.05 to 1.15; p=1.73×10-5), chronic ischaemic heart disease (OR=1.10, 95% CI: 1.05 to 1.15; p=1.52×10-5), myocardial infarction (OR=1.15, 95% CI=1.07 to 1.23, p=5.23×10-5), and hypercholesterolaemia (OR=1.08, 95% CI: 1.04 to 1.13, p=3.34×10-4). Findings from the TreeWAS analysis were generally consistent with that of PheWAS, with a number of more sub-phenotypes being identified. Results from IVW MR suggested that genetically determined high serum urate level was associated with increased risk of gout (OR=4.53, 95%CI: 3.64-5.64, p=9.66×10-42), CHD (OR=1.10, 95%CI: 1.02 to 1.19, p=0.009), myocardial infarction (OR=1.11, 95%CI:1.02 to 1.20, p=0.011) and decreased level of HDL-c (OR=0.93, 95%CI:0.88 to 0.98, p=0.004), but had no effect on RA (OR=0.92, 95%CI: 0.84 to 1.01, p=0.085) and ischaemic stroke (OR=1.03, 95%CI: 0.93 to 1.14, P= 0.582). Egger MR indicated pleiotropic effects on the causal estimates of DBP (P_pleiotropy=0.014), SBP (P_pleiotropy=0.003), CHD (P_pleiotropy=0.008), myocardial infarction (P_pleiotropy=0.008) and HDL-c (P_pleiotropy=0.016). When balancing out the potential pleiotropic effects in Egger MR, a causal effect can only be verified for gout (OR=4.17, 95%CI: 3.03 to 5.74, P_effect=1.27×〖10〗^(-9); P_pleiotropy=0.485). Sensitivity analyses on the GRSs of different groups of pleiotropic loci support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observed associations with cardiovascular/metabolic diseases. ConclusionsThis thesis presents a comprehensive investigation on the health outcomes in relation to SUA level. The causal relationship between high SUA level and gout is robustly verified in this thesis with consistent evidence from the umbrella review, the MR-PheWAS and the PWMR. The association of high SUA level with hypertension and heart diseases is supported by both the evidence from umbrella review and analyses conducted in this thesis, however, given the caveat of pleiotropy in the causal inference, a conclusion of causality on hypertension and heart diseases is not robust enough based on the current findings. Furthermore, the epidemiological evidence from the umbrella review indicated that high SUA level was associated with several components of metabolic disorders, and the analyses of the UK Biobank data identified a significant association with metabolic disorders and a sub-phenotype (hypercholesterolaemia). The causal inference in this study is limited by the common difficulty of pleiotropy caused by the use of multiple genetic instruments. Although we have performed sensitivity analysis by excluding the key pleiotropic locus, unmeasured pleiotropy and biases are still possible. In particular, unbalanced pleiotropy is recognised as an issue for the causal connections on the association between SUA level and hypertension. Other potential causal relevance of SUA level with respiratory diseases and ocular diseases is also worthy of further investigation. Overall, when taken together the findings from umbrella review, MR-PheWAS, PheWAS/TreeWAS analysis, MR replication and sensitivity analysis conducted in this thesis, I conclude that there are robust associations between urate and several disease groups, including gout, hypertensive diseases, heart diseases and metabolic disorders, but the causal role of urate only exists in gout. This study indicates that the observed associations between urate and cardiovascular/metabolic diseases are probably derived from the pleiotropic effects of genetic variants on urate and metabolic traits. Further investigation of therapies targeting the shared biological pathways between urate and metabolic traits may be beneficial for the treatment of gout and the primary prevention of cardiovascular/metabolic diseases